You will be given a definition of a task first, then some input of the task.
In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

One of the solutions that has been proposed for mitigating gender bias on the word embedding level is Counterfactual Data Augmentation (CDA) BIBREF25. We apply this method by augmenting our dataset with a copy of every dialogue with gendered words swapped using the gendered word pair list provided by BIBREF21. 
 Question: How does counterfactual data augmentation aim to tackle bias?
Output:
The training dataset is augmented by swapping all gendered words by their other gender counterparts